1.3. Establishment

Many perennial warm season grasses such as switchgrass are difficult to establish [17]. In an on-farm scale study, net energy value of switchgrass is largely determined by the biomass yield in established year [8]. Therefore, improving crop establishment is a very important step to successfully manage biofuel crops. There are many factors affecting establishment of perennial grasses; however, soil moisture and temperature are the most important ones, and many management practices are related to maintenance of adequate moisture and optimum temperature for seedling development and growth.

Seeding rate (pure live seeds): Typically recommended seeding rate in the US is 4-10 kg/ha for switchgrass based on the review of Parrish and Fike (2005) [17]. Sedivec et al. (2001) provided a detail recommendation for grass varieties for ND, ranging from 2 to 24 lb/ac, depending on species or varieties [42].

Seeding depth: The seeding depth may vary with soil types. However, seeding depth of grasses is generally shallower than cereal crops. For switchgrass in NE, seeding depth ranged from 1.5 cm to 3.0 cm in silt loam soil [43]. In SD, Nyoka et al. (2007) recommended not seeding deeper than 2.5 cm regardless of soil type [44].

Seeding date: Seeding date is largely determined by soil temperature and moisture. For warm season grasses, the ideal temperature for seed germination is between 20-30 oC if no dormancy [44, 46]. In SD, the recommended seeding date is early May to mid-June [44]. In VA, the planting date for switchgrass is much later than for corn but similar to that for millet or sorghum-sudangrass. In conventionally prepared seedbeds, June 1-15 was recommended [47]. In NE, study showed that planting switchgrass in mid-March can significantly increase seedling size as compared to late April and May [48]. Under NGP conditions, early seeding may provide benefit in terms of adequate soil moisture [48]. However, low soil temperature may be a factor for limiting germination and emergence of warm season grasses.

Timing an appropriate seeding date is also important for weed control. In a study conducted in Mississippi, Holmberg and Baldwin (2006) seeded switchgrass monthly from April to October and found that the months with minimum weed biomass were April and June. In addition, rainfall is also a very important factor for determining weed suppression for seeding switchgrass [49].

Seeding methods: switchgrass and other warm season grasses can be seeded under both conventional and no-till conditions. The ideal condition for conventional seeding should be a smooth, firm, clod-free soil for optimum seed placement with drills or culti-packer seeders [44]. The seedbed should be firm enough for good seed-soil contact and a consistent seeding depth [44, 47]. Since switchgrass requires warm weather for seeding, water loss during tillage could be a problem under dry and warm days. As a result, conventional seeding may not be ideal [47].

No-till helps to conserve soil moisture, requires less time and fuel, and eliminates the soil crusting frequently encountered in conventional seedbed [47]. In the literature, the results of comparison of conventional and no-till planting for warm season grass establishment are controversial. However, no-till planting frequently showed advantages over conventional tillage, in terms of soil and water conservation [17].

The warm season grasses can be seeded by drilling as well as broadcasting. For broadcasting method, cultipacking or rolling the seedbed after broadcasting is required to ensure that seeds are sufficiently covered by soil and to improve seed-to-soil contact [44].

Seed size (seed mass): Seed size varies considerably within cultivars as well as seedlots of a single cultivar [50]. In general, seed size is linearly related to seed mass or weight in many grasses and cereal crops. Large seeds normally have advantage over small seeds for germination and emergence [51], and seedling development [52]. Switchgrass seedlings grown from larger seeds developed adventitious roots more quickly than those from small seeds [52]. Even the seedling size associated to seed size was only evident at early stage [53], Vogel (2000) still suggested that selection of populations with larger seeds may improve seedling establishment in switchgrass [18].

Seedling vigor: Seedling establishment can be quantified by a more general term, seedling vigor. Greater seedling vigor refers to larger seedling size, greater ground cover and higher biomass at early stage. In addition to environmental factors, seedling vigor is believed to the single most important trait controlled by genetic variability in establishment capacity of perennial forage crops. Many researchers have used some measure of seedling vigor as a selection criterion to improve establishment capacity, while others have used more indirect measures, such as seed mass or germination rate [54]. As mentioned in the above, seed size is positively related to seeding vigor. However, other factors are also related to seedling vigor. For example, studies in cereal crops in Australia showed that embryo size significantly contributed to seedling vigor in barley [55]. In spring wheat, high protein content also contributed to seeding vigor.

Others: Application of arbuscular mycorrhizal fungi (AMF) has been shown to be effective for enhancing seedling yield and nutrient uptake in switchgrass [56-58]. Hanson and

Johnson (2005) showed that soil PH affected switchgrass germination and the optimum PH is 6.0 [46].

3.3. Typical linkage in logistics chain

As previously stated, biomass logistics is "a series of unit operations that begin with biomass standing in the field and ends with a stream of size-reduced material entering a bioenergy plant for 24/7 operation." This concept can be visualized as a chain (Figure 6), where the links are unit operations. The example in Figure 6 shows a logistics system moving bales from the field to SSLs, and then from these SSLs to a bioenergy plant. The dotted lines show various segments of the chain that are assigned to the several entities in the business plan. In this example, the segment identified "farmgate" is performed by the
feedstock producer, the segment labeled "SSL" is performed by a load-haul contractor; the segment identified as "Receiving Facility" is performed by the bioenergy plant. Figure 6 is one example; several other options are used in commercial practice. In next section, three commercial examples are given to show readers the range of options in a logistics system.

Farmgate operations

operations

Conveying^conversion

jnto plant^A^

Figure 6. Logistics chain for delivery of round bales to a bioenergy plant (The dotted lines identify segments of the chain which are performed by different business entities).

For down-flow BAC bed, though there was a decreasing tendency of bacteria adsorbed on activated carbon from top of the carbon layer to the bottom, the variation tendencies of ammonifying bacteria, anti-vulcanization bacteria and denitrifying bacteria are not exactly the same, among which ammonifying bacteria showed a decreasing tendency from top of the carbon layer to the bottom; anti-vulcanization bacteria showed an increasing tendency from top of the carbon layer to the bottom reaching a steady quantity; while denitrifying bacteria which located higher than 30cm of the carbon layer showed a gradual increasing tendency, however, after reaching the critical value of 30cm, it would decrease rapidly (shown in Fig. 18). As whole, aerobic bacteria is dominant in BAC bed.

vulcanization bacterialX’1) Denitrifying bactena(l 0/mL)

Figure 18. Distribution of biocommunity along BAC filter depth

As ammonifying bacteria is strictly aerobic, and denitrifying bacteria is amphitrophic, while anti-vulcanization bacteria is strictly anaerobic, the result shown in Fig. 18 is conducted by effects of two sides. Firstly, BAC is a kind of biofilm in essence, the thicker it gets, the more likely to create an anaerobic or hypoxia environment internal, which directly induces the existence of strictly anaerobic bacteria and amphitrophic bacteria. Secondly, variation of DO in activated carbon bed changes the distribution of biocommunity. Because aerobic bacteria is dominant in BAC bed, in the down-flow system, there is a decreasing tendency along the carbon layer (shown in Fig. 19), although, the concentration of DO in bottom of the layer is about 2-3mg/L, the flow inside BAC bed is laminar flow, which is not easy for the transmission of oxygen, hence, the bottom of activated bed shows anaerobic or anoxic characteristic, resulting in existence of amphitrophic bacteria and anaerobic bacteria.

During a steady operation of BAC process, a complex ecological system consists of bacteria, fungi, and algae which is from protozoa to metazoa is formed, which indicates the biofacies in BAC bed is of great abundance, and the ecological system of great diversity is fully developed which has a strong ability of avoiding the load impact. Part of the typical organisms inside BAC bed examined by microscope is shown in Fig. 20[54].

The stability and maturity of compost is essential for its successful application, particularly for composts used in high value horticultural crops [33]. Both terms are usually used interchangeably to describe the degree of decomposition and transformation of the organic matter in compost [34], despite the fact that they describe different properties of the composting substrate. Stability is strongly related to the degree to which composts have been decomposed to more stable organic materials [35]. Unstable compost, in contrast, contains a high proportion of biodegradable matter that may sustain a high microbial activity [36]. Typically, compost stability is evaluated by different respirometric measurements and/or by studying the transformations in the chemical characteristics of compost organic matter [6]. On the other hand, compost maturity generally refers to the degree of decomposition of phytotoxic organic substances produced during the active composting stage and to the absence of pathogens and viable weed seeds [37]. This property is often characterised by germination indexes [38] and/or by nitrification [6] and has been related to the degree of compost humification. Wu et al. [37] reported that the low CO2 evolution is not always an indicator of a non-phytotoxic compost, which suggests that a stable compost may not always be at a level of maturity suitable for its use as a growing medium for certain species of plant.

Several authors highlight that there is no one single method that can be applied successfully for determining compost stability mainly due to the wide range of raw materials used to produce compost, as reviewed by [6]. Therefore, the integration of different parameters seems to be the most reliable option for evaluating the stability/maturity stage of composted materials. For instance, physical parameters including temperature, odor and color constitute a very simple and rapid method for stability evaluation, giving a general idea of the decomposition stage reached; however, little information is achieved as regards to the degree of maturation. In addition, chemical parameters including pH, electrical conductivity, cation exchange capacity (CEC), the ratios of C to N and NH4+ to NO3- and humification parameters have also been widely used as indicators of stability [39]. Nevertheless, several drawbacks have been found regarding these parameters, thereby preventing their use as accurate indicators. According to Wu et al. [37], pH and electrical conductivity may be used to monitor compost stabilisation, as long as the source waste composition is relatively consistent and other stability tests are conducted. Moreover, Namkoong et al. [40] established that the C to N ratio could not be considered as a reliable index of compost stability, as it changed irregularly with time. In fact, when wastes rich in nitrogen are used as the source material for composting, like sewage sludges or manures, the C to N ratio can be within the values of a stable compost even though it may still be unstable. Zmora-Nahum et al. [34] reported a C to N ratio lower than the cutoff value of 15 very early during the composting of cattle manure, while important stabilisation processes were still taking place. The increase in CEC with composting time is related to the formation of carboxyl and phenolic functional groups during the humification processes; however, the wide variation in CEC values among the initial substrates prevent to establish a threshold level and to use it as a stability indicator [41].

Biological parameters such as respiration rates (CO2 evolution rate and/or O2 uptake rate) and enzyme activities have been proposed to measure compost stability [6-39]. The principle of the respirometric tests is that unstable compost has a strong demand for O2 and high CO2 production rates as a consequence of the intensive microbial development due to the presence of easily biodegradable compounds in the raw material. Then, as composting proceeds, the decrease in the amount of degradable organic matter is accompanied by a decline in both O2 and CO2 respirometry. The Solvita test, which measures CO2 evolution and ammonia emissions simultaneously have been found to be a simple and easily used procedure for quantifying soil microbial activity in comparison with both titration and infrared gas analysis [42]; this test has also been used for determining the stability degree in diverse composts [43]. Enzymatic activities have also been found suitable as indicators of the state and evolution of the organic matter during composting, as they are implicated in the biological and biochemical processes through which the initial organic substrates are transformed (Tiquia, 2005). Important enzymes during composting are related to the C-cycle (cellulases, P-glucosidase, P-galactosidase), the N-cycle (protease, urease, amidase) and/or the P-cycle (phosphatase) [44]. These latter authors established that the formation of a stable enzymatic complex, either in moist or air-dried compost samples, could represent a useful index of stabilisation. Additionally, enzymatic activities, especially dehydrogenases, are considered easy, quick and cheap stability measurements [36]; however, the wide range of organic substrates involved in the composting process makes it difficult to establish general threshold values for these parameters. The hydrolysis of fluorescein diacetate (FDA), which is a colourless fluorescein conjugated that is hydrolyzed by both free (exoenzymes) and membrane bound enzymes [45], has been suggested as a valid parameter for measuring the degree of biological stability of the composting material, as it showed a good correlation with other important stability indexes [46]. The analysis of phospholipid fatty acid (PLFA) composition has also been proposed for determining compost stability [47-48]. These authors found a positive correlation between the proportion of PLFA biomarkers for Gram­positive bacteria and the germination index during the maturation of composting of poultry manure and cattle manure, respectively. The strength of this lipid-based approach, as compared to other microbial community assays, is that PLFAs are rapidly synthesized during microbial growth and quickly degraded upon microbial death and they are not found in storage molecules, thereby providing an accurate ‘fingerprint’ of the current living community [49]. The potential ability of the microbial community to utilise select carbon sources by determining the community-level physiological profiles (CLPPs) with the Biolog® Ecoplate has also been considered for compost stability testing [50]. The principle is that compost extracts are inoculated onto microtiter plates that contain 31 different C substrates [51-52]. Ultimately, molecular techniques are becoming increasingly useful in composting research. For example, as in reference [32] the authors used three different cultivation-independent techniques based on 16S rRNA gene sequences, i. e. PCR — denaturing gradient gel electrophoresis (DGGE), clone libraries, and an oligonucleotide microarray (COMPOCHIP), in order to evaluate the dynamics of microbial communities during the compost-curing phase. Specific compost-targeted microarrays are suitable to investigate bacterial [53-54] and fungal community patterns [55], including plant growth promoting organisms and plant and human pathogens.

Phytoplankton analysis is possible by a simple Kolkwitz chamber. Except deposited plankton in sample bottle, liquid at the top pour a few millilitres and centrifuged and their volumes are measured in sedimentation tubes then they are transferred to Kolkwitz chamber for analysis. In this method, phytoplankton analysis is the first, semi-field analysis of Kolkwitz chamber is the second and the third one is the analysis of the various fields.

Biovolume was estimated in the measurement of phytoplankton biomass. Phytoplankton were emulated to the geometric shapes like sphere, cylinder and cone and necessary measurements were taken from the phytoplankton while counting [32]. Geometric shapes and calculations used for calculating biovolume was done according to the formulas (Table 1) stated by [12] and [16]. After calculating average volume of every species, total volume were calculated by multiplying with the number of species. Following formula was used to calculate total cell volume of phytoplankton [31];

HH = £ (HNixSHi)

i=1

Here;

HH= Total biovolume of plankton (mm/l),

HNi= the number of organisms belongs to i. species /l,

SHi= Average cell volume of i. species.

Biovolume is calculated by assuming cell volume is equal to 1mg age weight/m3 algal biovolume for 1mm/ m3 [33].

Biovolume Samples

ж 3 Crucigeniella apiculata

V ~ ~6′ a Gomphosphaeria sp.

Anabeana sp.

Coelastrum microporum Actinastrum hantzschii Dinobryon divergens Cryptomonas sp. Pandorina sp.

Trachelomonas caudata Peridinium sp. Botryococcus braunii Cocconeis placentula Phacus tortus

Cyclotella sp. Mougeotia sp.

Samples____

Chroomonas sp.

Asterionella sp. Synedrasp. Merismopedia sp. Epithemia zebra var. saxonica

Pediastrum sp. Navicula sp.

Cymatopleura sp.

Nitzschia sp.

Phaeodactylum sp.

3. Case studies conducted to phytoplankton biomass

There is a significant correlation between biomass of phytoplankton with the concentration of phosphorus. Changes are seen in phytoplankton biomass or production rate with the changes of the concentration of phosphorus in the lakes. It has been showed in the field studies that light and temperature play a significant role in the relationship between the extinction rate and biomass and carrying capacity of the composition of species [34].

In a study conducted in Lake Erie, samples have been collected from three different points in spring, summer and fall season for five years; 49 species were determined at the end of the study [35].

In a lake with 25000 km2 surface area, phytoplankton biomass showed local changes and it was determined as 1.88 ± 0.12 g/m3, 1.04 ± 0.07 g/m3 and 0.63 ± 0.071 g/m3 in west, mid and east part of the lake respectively. It was determined that algal biomass decreased and biomasses of Aphanizomenon flos-aque, Stephanodiscus binderounus, S. niagarae, S. tenuis, Rhodomonas minuta decreased in the rate of 70-98% from 1970 to 1983-1987 [35].

Phytoplankton communities and distributions were investigated from the samples taken weekly from two dam lake with different nutritient levels in Sicily. Lake Arancio is a shallow eutrophic lake and Lake Rosamarina is a deep mezotrofik lake. It was stated that the increment in the concentration of nutrients in Lake Arancio doesn’t change the composition of phytoplankton but increase biomass of phytoplankton [36].

In a study examining 27 lakes in Russia; plankton biomass and total phosphorus concentration were investigated and it is stated that total phosphorus concentration changes between 10-137 mg/m3 and biomass changes between 0.4-20 g/m3. Total 160 phytoplankton species were identified and it was reported that most of those are belong to the blue-green algae and euglenophyceae classes. The lakes were determined to be hypertrophic and acidic [37].

In a study conducted with Lake Dorani, it was determined that the most common class in the lake was chlorophyceae followed by cyanophyceae. Total phytoplankton biomass is found similar to eutrophic lakes. While, nanoplankton biomass constitute 90% of total phytoplankton biomass in spring but it is 10% throughout the year. It is found that total biomass is high in summer, low in winter and changes between 0.43-30.30 mg/l [38].

Seasonal changes of phytoplankton communities in Lake Managua are investigated, it is reported that blue-green algae are dominant during the research period. Seasonal biomass are measured monthly for two years and the lowest phytoplankton biomass was found at the end of the rainy seasons (October, November). In short term studies (3-14 days), important changes in biomass were reported. Nutrient levels of the lake were estimated as hypertrophic according to chlorophyll a value (79 pg/l yearly average, 1987-1988) [39].

During the research conducted with Lake Beysehir, diatoms and green algae are found dominant. throughout the research Aulacoseira granulata and Cyclotella meneghiniana from centric diatom, Asterionella formosa, Cocconeis placentula, Cymbella affinis and Ulnaria acus from pennate diatoms, Monoraphidium spp., Mougeotia sp. and Scenedesmus linearis from Chlorophyta, Dinobryon divergens from Chrysophyta and Cryptomonas marssonii, Rhodomonas lacustris from Cryptophyta, Merismopedia glauca from Cyanophyta are commonly found and partly numerical increases are observed. Phytoplankton biomass in the lake changes between 0.40±0.11 and 6.43±1.00 mg/l. The lake is in mesotrophic nutrients level according to average phytoplankton biomass (1.98±0.2 mg/l) and it is in good ecological quality class [40].

An acid-base biomass hydrolysates solution containing 48.5 g/L of soluble solids was added into a glucose medium at predetermined percentage of cell debris to glucose from 0 to 40%. The glucose medium without biomass hydrolysates was run in parallel as controls. As shown

in Table 1, the concentrations of both cell mass and PH A content, after 48 hours cultivation, were substantially higher than those of the control. The overall cell growth yield (Yx/s) and PHA formation yield (Yp/s) are calculated from the amounts of cell mass and PHA formed in 48 hours based on the initial concentration of glucose. The relative yields (Yx’ and Yp’) based on the controls were increased by 100 — 300%. More interestingly, the inhibitory effect of acid biomass hydrolysates was not observed in the use of acid-base hydrolysates, and the load of cell debris to glucose can be increased to about 39 wt%. Most likely, the unknown inhibitors in acid hydrolysates were further decomposed into less inhibitory hydrolysates in the base treatment. Since the acid biomass hydrolysates contained primarily the cytoplasm proteins released from the damaged cells, the soluble proteins might adversely affect cell growth at high concentrations. After being hydrolyzed in base solution into peptides and amino acids, the small molecule hydrolysates become less inhibitory and more usable to the cells.

We recall that our aim is to estimate on-line both parameters and unmeasured variables x, xd, based on the measurements. One can immediately see from equations (3) that parameters (m, a, k) cannot be reconstructed observing the system at steady state. Nevertheless, considering the derivative y’ of ц with respect to s and deriving the outputs:

yi = (—y(yi) + ka) x, y2 = (y(yi) — a) x

yi = (y (yi) — m) yi — Ц(yi) xyl,

y2 = (y(yi) — m) y2 + ц'(yi) xy1.,

one obtains explicit expression of the parameters and unmeasured state variable as functions of the outputs and its derivatives, away from steady state:

from which one deduces the observability of the system.

For example, maximum at-plant storage for a 3-day supply is, 3.75 racks/h times 72 h, or 270 racks. Racks will be stacked two high in "units" with two rows of 24 spaces each (Figure 12), thus there are 48 storage spaces in each unit. Each unit stores 96 racks. Three units are required for 270-racks storage.

3.75 racks / h x 72 h = 270 racks

If 7-day at-plant storage is required, the total number of racks increases to 630 racks, or seven 96-rack units as shown in Figure 14. This implementation of a 7-day supply is not believed to be a cost effective choice, not only because of the capital investment in the racks, but also because of the forklift operating time required to cycle back and forth over a storage area this large. This is a key issue—the larger the at-plant storage, the lower the forklift productivity (ton/h), and thus the higher the forklift cost ($/ton).

The rack system competes best when the racks are filled and emptied as many times as possible in a given time period, not when they act as storage units. Other multi-bale handling units are more suitable for a larger at-plant storage like the one shown in Figure 14.

When bromine exists in water, bromine will be oxidized into hypobromous acid by ozone, and then hypobromous acid will be oxidized into bromate, which has carcinogenicity to human body[67]. When ammonia nitrogen and amino acid exist at the same time in water, the reaction speed of bromate and ammonia nitrogen is faster, thus, dosing little amounts of ammonia would often restrain the production of organobromine compounds. Moreover, when bromine concentration is higher in the water, cyanogen bromide becomes the main ozonation byproduct[68]. Byproducts caused by bromide are shown in Table 8.

Ozonation byproducts could be removed by adsorption of activated carbon. Meanwhile, ozonation and hypermanganate hybrid method will reduce the amount of byproducts. Taking more ozone dosing points can shorten the mean contact time and reduce the residual ozone concentration, thus bromate production will be decreased accordingly. Moreover, dosing acid to lower pH, ammonia, excessive H2O2 and OH scavenger to water may reduce BrO3- production[69] as well.

The presence of oxygen in cellulose, hemicelluloses and lignin is the primarily reason for biomass derived bio-oil to be highly functionalized. Cellulose and hemicellulose have common structural building blocks which are glucose and xylose respectively. Cellulose which is the most abundant component in terrestrial biomass is a crystalline polymer, and therefore, is quite difficult to chemically transform. Lignin which is abundant in biomass is mainly an amorphous poly aromatic polymer. Because of this complex heterogeneity of biomass, the liquid derived from the pyrolysis or liquefaction process contains a variety of different chemical species which can be roughly estimated to be around 400.

1.1. Importance of deoxygenation

The properties of bio-oil are significantly affected by its chemical composition. Unlike crude petrolium, bio-oil constituents have numerous functional groups as shown in figure (4). The high functionality decreases the stability of the oils and results in polymerization. A direct consequence is the increase of viscosity of bio-oils with time which in turn raises concerns about the feasibility of using bio-oil products as substitutes for petrochemical fuels. In addition to instability, low pH values and the presence of a high water content and ash

content are considered problematic [29, 30]. Additionally, the presence of oxygenates and water in the bio-oil reduces the heating value. Consequently, upgrading is necessary to make it useful as a fuel.

As shown in the figure (5) upgrading improves the H/C molar ratio together with increasing the heating value. In contrast, crude petroleum is lean in oxygenated compounds and therefore, most of the chemistries developed to date are based on adding functional groups to increase its activity. As a result, there is only limited knowledge on techniques available to remove functionality from highly oxygenated compounds. However the upgrading of bio-oil by removing of oxygen (deoxygenation) is considered as one of the most intricate challenges we face today in getting bio-oil to a form that is applicable as a fuel intermediate.

As discussed above, chemical species present in biomass derived liquid bio-oil can be categorized into alcohols, aldehyde / ketones, carboxylic acids, phenols and furans. Therefore the upgrading process of bio-oil involves removal of various oxygenated species and can be collectively called as deoxygenation. The deoxygenation process predominantly involves three reaction classes, i. e., dehydration, decarbonilation and decarboxylation.